PID controller for automatic voltage regulator using teaching–learning based optimization technique

The present work presents teaching–learning based optimization (TLBO) algorithm as an optimization technique in the area of tuning of the classical controller installed in automatic voltage regulator (AVR). The proposed TLBO algorithm is applied with an aim to find out the optimum value of proportional integral derivative (PID) controller gains with first order low pass filter installed in the AVR. The voltage response of the AVR system, as obtained by using the proposed TLBO based PID controller with first order low pass filter, is compared to those offered by the other algorithms reported in the recent state-of-the-art literatures. The advantage of using this control strategy may be noted by providing good dynamic responses over a wide range of system parametric variations. For on-line, off-nominal operating conditions, fast acting Sugeno fuzzy logic technique is applied to obtain the on-line dynamic responses of the studied model. Furthermore, robustness analysis is also carried out to check the performance of the designed TLBO based PID controller. An analysis, based on voltage response profile, has been investigated with the variations of the model parameters. The simulation results show that the proposed TLBO based PID controller is a significant optimization tool in the subject area of the AVR system. The essence of the present work signifies that the proposed TLBO technique maybe, successfully, applied for the AVR of power system.     

A GA optimization for lithium–ion battery equalization based on SOC estimation by NN and FLC

An intelligent control proposal for battery equalization is presented by genetic algorithm optimization integrated with fuzzy logic control–neural network algorithm. First, an effective two-stage DC/DC converter architecture is developed, which pave the way for the hardware module. Then, an equivalent circuit model in weighted combination with ampere-hour counting method is adopted by fuzzy logic control scheme to obtain static SOC estimation. Then the dynamic battery SOC is precisely estimated on basis of static SOC by means of neural network. The most important is the genetic algorithm optimization for battery equalization to improve the energy efficiency and time efficiency of the equalization system. Finally, certification of simulation is demonstrated to validate the proposed novel equalization scheme.     

Short term load forecasting using wavelet transform combined with Holt–Winters and weighted nearest neighbor models

Short term load forecasting (STLF) is an integral part of power system operations as it is essential for ensuring supply of electrical energy with minimum expenses. This paper proposes a hybrid method based on wavelet transform, Triple Exponential Smoothing (TES) model and weighted nearest neighbor (WNN) model for STLF. The original demand series is decomposed, thresholded and reconstructed into deterministic and fluctuation series using Haar wavelet filters. The deterministic series that reflects the slow dynamics of load data is modeled using TES model while the fluctuation series that reflects the faster dynamics is fitted by WNN model. The forecasts of two subseries are composed to obtain the 24 h ahead load forecast. The performance of the proposed model is evaluated by applying it to forecast the day ahead load in the electricity markets of California and Spain. The results obtained demonstrate the forecast accuracy of the proposed technique.     

Optimal distributed generation location and size using a modified teaching–learning based optimization algorithm

In this paper, a method which employs a Modified Teaching–Learning Based Optimization (MTLBO) algorithm is proposed to determine the optimal placement and size of Distributed Generation (DG) units in distribution systems. For the sake of clarity, and without loss of generality, the objective function considered is to minimize total electrical power losses, although the problem can be easily configured as multi-objective (other objective functions can be considered at the same time), where the optimal location of DG systems, along with their sizes, are simultaneously obtained. The optimal DG site and size problem is modeled as a mixed integer nonlinear programming problem. Evolutionary methods are used by researchers to solve this problem because of their independence from type of the objective function and constraints. Recently, a new evolutionary method called Teaching–Learning Based Optimization (TLBO) algorithm has been presented, which is modified and used in this paper to find the best sites to connect DG systems in a distribution network, choosing among a large number of potential combinations. A comparison between the proposed algorithm and a brute force method is performed. Besides this, it has also been carried out a comparison using several results available in other articles published by others authors. Numerical results for two test distribution systems have been presented in order to show the effectiveness of the proposed approach.     

Nanoelectronic circuit elements based on nanoscale metal–molecular networks

Journal of Computational Electronics - We report a density functional non-equilibrium Green's function study of the electronic transport properties of nanoscale networks composed of Au metal...     

A practical design sliding mode controller for DC–DC converter based on control parameters optimization using assigned poles associate to genetic algorithm

This paper presents a simple and systematic approach to design sliding mode controller for buck converters. DC/DC switching converters have been used in many applications in recent years. The Sliding Mode Control (SMC) based on Variable Structure System (VSS) theory has been examined for buck switch mode converter type. To obtain a sliding regime, the appropriate sliding surface is selected by assigning poles combined with genetic algorithm. The associated practical problems and the proposed solutions are detailed; like fixed frequency by using pulse width modulation (PWM) as solution and overshoot limitation of the inductor current by using current limiter. A simple and easy to follow design procedure is also described. Simulation results are presented to illustrate the design procedure. The simulation results of the proposed SMC strategy are compared with the adaptive terminal sliding mode control (ATSMC) and PID-SMVC strategies. It is shown that the proposed SMC exhibits a considerable improvement in terms of a faster output voltage response during load changes.     

Poisson–Nernst–Planck model for an ionic transistor based on a semiconductor membrane

In this paper we developed a Poisson–Nernst–Planck model of an ionic current flowing through a nanopore in a layered solid-state membrane made of a single highly-doped \(n\) -Si layer sandwiched between two thick oxide layers which we call the ionic transistor. We studied this layered membrane for a range of source-drain voltages while keeping the gate (the semiconductor membrane) voltage fixed at a certain value, which was later varied too. We find that for this ionic transistor to be effective in controling the ion fluxes through the nanopore, the gate voltage must be kept relatively large. Another solution could be to increase the surface negative charge on the membrane or to replace the outer oxide layers with the semiconductor material, such as the \(p\) -Si material. The developed model can be applied to study ionic filtering and separation properties of membranes of different composition and nanopore geometries.     

Outage data analysis of utility power transformers based on outage reports during 2002–2009

Statistical analysis of failures and forced outages of power transformers constitute an important basis for asset management of these transformers. Results of the statistical analysis can be used, for example, to enhance utility reliability, influence transformer design and technology, and improve maintenance and condition monitoring practices. In addition, various methods for transformer reliability evaluation require that the expected values of component outage rates, outage durations, and repair durations be known. In this paper, outage data are obtained from the Egyptian Electricity Transmission Company (EETC). This work presents outage data analysis over eight years, from 2002 to 2009, for 1922 (average number) transformers in voltage populations ranging from 33 kV to 500 kV and MVA rating from 5 MVA to 500 MVA. Forced outages due to correct and false action of transformer’s protection systems are carefully considered. Outage data analysis is conducted according to two basic phases. In the first phase, failure and repair analysis of transformers is performed while in the second phase impact of transformer outages on customers is assessed. Percentage average number of failures (%AANF) and annual average repair time (AART) per transformer are used to represent the failure and repair data of power transformers. Two indicators are used to represent the impact of transformer outages on customer interruptions. These indicators are the annual average interrupted MW (AAIMW) and annual average customer-interruption duration (AACID). A summary of the main outcomes of the work presented in this paper is provided in the conclusions’ section; however, it is worthy to be mentioned here that the fire-fighting systems are responsible for the highest number of false trips in all voltage subpopulations except the 220 kV subpopulation where the dominant cause of false trips is the busbar protection. Therefore, it is recommended to improve the maintenance and design of this protection equipment to reduce the failure rate of power transformers.     

Energy procurement management for electricity retailer using new hybrid approach based on combined BICA–BPSO

These smaller consumers usually prefer to purchase at a constant price that is adjusted at most a few times per year. Electricity retailers are in business to bridge the gap between the wholesale market and these smaller consumers. This paper proposes a hybrid approach based on binary imperialist competitive algorithm (BICA) and binary particle swarm optimization (BPSO) to find the optimal energy procurement for electricity retailer with multiple procurement options such as self-generating facility, bilateral contracts and pool market purchase in restructured electricity markets. The solutions of these problems provide adequate information to obtain an energy procurement strategy for retailers. Also, the results of proposed hybrid approach are compared with conventional ICA and PSO methods. Test results show that the proposed hybrid approach is more effective and has higher capability in finding optimum solutions in comparison to ICA and PSO methods. A case study is used to illustrate the efficiency of the proposed hybrid approach.     

Wireless charging for cardiac pacemakers based on class-D power amplifier and a series–parallel spider-web coil

Biomedical implants (BMIs) and biomedical sensors (BMSs) help to improve quality of life, detect diseases, provide monitoring of vital signs, and take over the role of malfunctioning organs. These implants and sensors require continuous battery power to work effectively, but the batteries used are restricted by their limited capacity and lifetime. This research reported here involved the design and implementation of a wireless charging system based on a series–parallel spider-web coil (WPT-SP-SWC), specifically for cardiac pacemakers. The experimental design investigated several important parameters, including air gap, applied source voltage, coil size, and operating frequency. Performance metrics were evaluated in terms of output DC voltage, delivered output power, and power transfer efficiency. The target voltage was 5 V, which is adequate to charge a BMI such as a pacemaker, and three source voltages (5, 15, and 25 V) were tested. The design was examined at six operating frequencies, ranging from 1.78 MHz to 6.78 MHz. The most favorable results were achieved at 1.78 MHz. Power transfer efficiencies at a 10 mm air gap were 95.75% and 92.08% for applied voltages of 5 V and 15 V, respectively. The effectiveness of the proposed system was also validated by comparing the findings with previous articles.     

Fuzzy bilinear state feedback control design based on TS fuzzy bilinear model for DC–DC converters

This paper proposes a fuzzy bilinear state feedback controller based on Tagaki–Sugeno (TS) fuzzy bilinear model for DC–DC converters. The DC–DC converters can be approximated to bilinear model via Taylor series expansion. For achieving the output voltage regulation, we formulate the extended system of bilinear DC–DC converter model, one of whose state variables is the error between the output voltage and the reference output voltage. The extended system can be described to TS fuzzy bilinear model. We design a fuzzy bilinear state feedback controller to track the reference output voltage. The closed loop system is globally asymptotically stable via the proposed controller. Lyapunov theory is employed to guarantee the stability of the closed loop system. Numerical examples are given to demonstrate the validity of our proposed control approach with comparative results.     

A new switched-capacitor/switched-inductor–based converter with high voltage gain and low voltage stress on switches

In this paper, a nonisolated transformerless switched-capacitor/switched-inductor–based dc-dc converter with high voltage gain is proposed. The proposed converter has low voltage stress on switches and diodes which leads to decrease the switching losses. The voltage gain of the proposed converter can be increased by adding more diode-capacitor modules at output side; therefore, the proposed converter has expandable structure. The proposed converter has higher voltage gain and lower voltage stress on switches comparing with the other similar switched-capacitor–based converters. The proposed converter uses two switches with the same switching pattern. Therefore, its switching pattern is not complicated. In this paper, the proposed converter is analyzed during a switching period which has two operating modes. Moreover, the average current through the switches, diodes, and inductors; voltage stress on switches and diodes; voltage gain; maximum and minimum current of switches and diodes; and the efficiency curve are calculated. Finally, in order to verify the accuracy performance of the proposed converter, a 530-W 40 to 920-V prototype is implemented practically.     

Gradient-based recursive parameter estimation for a periodically nonuniformly sampled-data Hammerstein–Wiener system based on the key-term separation

The identification of the Hammerstein–Wiener (H-W) systems based on the nonuniform input–output dataset remains a challenging problem. This article studies the identification problem of a periodically nonuniformly sampled-data H-W system. In addition, the product terms of the parameters in the H-W system are inevitable. In order to solve the problem, the key-term separation is applied and two algorithms are proposed. One is the key-term-based forgetting factor stochastic gradient (KT-FFSG) algorithm based on the gradient search. The other is the key-term-based hierarchical forgetting factor stochastic gradient (KT-HFFSG) algorithm. Compared with the KT-FFSG algorithm, the KT-HFFSG algorithm gives more accurate estimates. The simulation results indicate that the proposed algorithms are effective.     

A novel high speed Artificial Neural Network–based chaotic True Random Number Generator on Field Programmable Gate Array

It is well observed that cryptographic applications have great challenges in guaranteeing high security as well as high throughput. Artificial neural network (ANN)–based chaotic true random number generator (TRNG) structure has not been unprecedented in current literature. This paper provides a novel type of high-speed TRNG based on chaos and ANN implemented in a Xilinx field-programmable gate array (FPGA) chip. The paper consists of two main parts. In the first part, chaos analyses of Pehlivan-Uyaroglu_2010 chaotic system (PUCS) have been accomplished to prove that PUCS operates in chaotic regime. So PUCS can be an efficient alternative to the entropy source for classical TRNGs. In the second part, the hardware design of the proposed TRNG has been created using VHDL in Xilinx platform. As a result, the implemented TRNG offers throughput up to 115.794 Mbps. Besides, the generated random numbers have been tested with the FIPS 140-1 and NIST 800.22 test suites. The high quality of generated true random numbers have been confirmed by passing all randomness tests. The results have shown that the proposed system can provide not only high throughput but also high quality random bit sequences for a wide variety of embedded cryptographic applications.     

Weighted couple-group consensus for a kind of discrete heterogeneous multiagent systems in cooperative–competitive networks based on self-adaptive controller

In this article, the weighted couple-group consensus is studied for a kind of discrete heterogeneous multiagent systems with time delays. Based on self-adaptive controller and cooperative–competitive relation, a novel weighted couple-group protocol is proposed for this system without satisfying the in-degree balance of the vertex. By applying complex frequency method, matrix analysis and graph algebraic theory, some sufficient conditions are given to ensure the success of couple-group consensus under directed or undirected topology. The upper limit of the input time delay can be computed according to the weighted parameters. Some simulation examples show the validity of the achieved results.     

A hybrid approach based on IGDT–MPSO method for optimal bidding strategy of price-taker generation station in day-ahead electricity market

This paper considers a price-taker generation station producer that participates in a day-ahead market. The producer behaves as a price-taker participant in the day-ahead electricity market. In electricity market, the price-taker producer could develop bidding strategies to maximize own profits. While making optimal bidding strategy, the market price uncertainty needs to be considered as they have direct impact on the expected profit and bidding curves. In this paper, a hybrid approach based on information gap decision theory (IGDT) and modified particle swarm optimization (MPSO) is used to develop the optimal bidding strategy. Information gap decision theory is used to model the optimal bidding strategy problem. It assesses the robustness/opportunity of optimal bidding strategy in the face of the market price uncertainty while price-taker producer considers whether a decision risk-averse or risk-taking. The optimization problems to delivering IGDT approach are solved using MPSO. It is shown that risk-averse or risk-taking decisions might affect the expected profit and bidding curve to day-ahead electricity market. The IGDT–MPSO method is illustrated through a case study and compared to IGDT–MINLP method.     

A simulation study of short channel effects with a QET model based on Fermi–Dirac statistics and nonparabolicity for high-mobility MOSFETs

In this paper, the quantum confinement and short channel effects of Si, Ge, and \(\hbox {In}_{0.53}\hbox {Ga}_{0.47}\)As n-MOSFETs are evaluated. Both bulk and double-gate structures are simulated using a quantum energy transport model based on Fermi–Dirac statistics. Nonparabolic band effects are further considered. The QET model allows us to simulate carrier transport including quantum confinement and hot carrier effects. The charge control by the gate is reduced in the Ge and \(\hbox {In}_{0.53}\hbox {Ga}_{0.47}\)As bulk n-MOSFETs due to the low effective mass and high permittivity. This charge control reduction induces the degradation of short channel effects. In double-gate structures, different improvements of drain induced barrier lowering (DIBL) and subthreshold slope (SS) are seen. The double-gate structure is effective in the suppression of DIBL for all channel materials. The SS degradation depends on channel materials even in double-gate structure.